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Between May 25, 2023 and June 21, 2023, we hosted the inaugural four-week High-Performance Computing Summer Institute at Jackson State University. This endeavor was made possible through the support of a three-year NSF CISE-MSI grant. The primary objective of this Summer Institute revolved around the engagement, education, and empowerment of minority and underrepresented students in the realm of High-Performance Computing (HPC) within the field of engineering. Nine undergraduate students with diverse background were recruited to participate in this program.  Throughout the program, we immersed these students in a comprehensive curriculum that covered various critical facets of HPC. This curriculum encompassed hands-on instruction in Linux operating system command-line operations, C programming within the Linux environment, fundamental HPC concepts, parallel computing utilizing the Message Passing Interface (MPI) library, and GPU computing through OpenCL. Additionally, we delved into foundational aspects of fluid mechanics, geometric modeling, mesh generation, flow simulation via our in-house flow solvers, and the visualization of solutions. At the end of the program, every participant was tasked with delivering an oral presentation and submitting a written report encapsulating their acquired knowledge and experiences during the program. We are excited to share a detailed overview of our program's implementation with our audience. This includes insights into our utilization of ChatGPT to enhance C programming learning and our suggestion of the NSF ACCESS resources to gain access to HPC systems. We are proud to announce that the program has achieved remarkable success, as evidenced by the positive feedback we received from the participants. 

This paper presents a robust mesh moving solver developed to address moving boundary problems. Crucially, the resulting deformed mesh retains the same topology as the original mesh without being overly distorted. The mesh is treated as an elastic material, and the deformation of the computational domain resulting from moving boundaries is determined by solving the equilibrium linear elasticity equations. The linear elasticity equations are discretized by the classic Galerkin finite element method and solved by the block conjugate gradient iterative method. To maintain the quality of the mesh after motion, the Young's modulus of each element is weighted by the reciprocal of the distance between the element center and the moving boundaries. The effectiveness of this approach is demonstrated through a set of 2D and 3D test cases featuring prescribed translational and/or rotational motion of the embedded object. The method is now ready for integration into our existing in-house CFD solvers. 

Multi-principal element alloys (MPEAs) exhibit outstanding strength attributed to the complex dislocation dynamics as compared to conventional alloys. Here, we develop an atomic-lattice-distortion-dependent discrete dislocation dynamics framework consisted of random field theory and phenomenological dislocation model to investigate the fundamental deformation mechanism underlying massive dislocation motions in body-centered cubic MPEA. Amazingly, the turbulence of dislocation speed is identified in light of strong heterogeneous lattice strain field caused by short-range ordering. Importantly, the vortex from dislocation flow turbulence not only acts as an effective source to initiate dislocation multiplication but also induces the strong local pinning trap to block dislocation movement, thus breaking the strength-ductility trade-off.

 

Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy.

 

While variation in mean annual precipitation (MAP) of the native habitat of a species has been shown to determine the ability of a species to resist a hydraulic decrease during drought, it remains unknown whether these variations in MAP also influence the ability of a species to recover and survive drought. Leaf hydraulic and gas exchange recovery following drought and the underlying mechanisms of these responses in sixCaraganaspecies from habitats along a large precipitation gradient were investigated during rehydration in a common garden. The gas exchange of species from arid habitats recovered more rapidly during rehydration after mild, moderate and severe drought stress treatments than species from humid habitats. The recovery of gas exchange was not associated with foliar abscisic acid concentration, but tightly related to the recovery of leaf hydraulic conductance (K_leaf). The recovery ofK_leafwas associated with the loss ofK_leafduring dehydration under mild and moderate drought stress, and to leaf xylem embolism formation under severe drought stress. Results pointed to the different ability to recover in gas exchange in sixCaraganaspecies post‐drought is associated with the MAP of the species in its native habitat.

 

Abstract This paper reports the development of a numerical solver aimed to simulate the interaction between the space charge (i.e. ions) distribution and the electric field in liquid argon time projection chamber (LArTPC) detectors. The ion transport equation is solved by a time-accurate, cell-centered finite volume method and the electric potential equation by a continuous finite element method. The electric potential equation updates the electric field which provides the drift velocity to the ion transport equation. The ion transport equation updates the space charge density distribution which appears as the source term in the electric potential equation. The interaction between the space charge distribution and the electric field is numerically simulated within each physical time step. The convective velocity in the ion transport equation can include the background flow velocity in addition to the electric drift velocity. The numerical solver has been parallelized using the Message Passing Interface (MPI) library. Numerical tests show and verify the capability and accuracy of the current numerical solver. It is planned that the developed numerical solver, together with a Computational Fluid Dynamics (CFD) package which provides the flow velocity field, can be used to investigate the space charge effect on the electric field in large-scale particle detectors.